Differences between Faster versus Slower Components of Convectively Coupled Equatorial Waves

Abstract

This paper describes an analysis of multiyear satellite datasets that subdivide two halves (faster and slower) of the space-time spectral signal peaks corresponding to convectively coupled equatorial waves such as Kelvin and inertia-gravity waves [n 5 0 eastward inertia-gravity wave (EIGn0 wave), and n 5 1 and n 5 2 westward inertia-gravity waves (WIGn1 and WIGn2 waves, respectively)]. The faster (slower) component of an equatorial wave is defined as that which has a spectral signal peak in the regions with deeper (shallower) equivalent depths. The data obtained from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (TRMM-PR) are composited around space-time-filtered equatorial-belt data from the TRMM-3B42 rainfall product to separately estimate the convective and stratiform rainfall modulations.Results indicate that the faster components of WIGn1 and WIGn2 waves modulate convective rain relatively more (and stratiform rain relatively less) than their slower counterparts. For Kelvin and EIGn0 waves, however, there is no significant difference in the rainfall modulation between their faster and slower components. A space-time cospectral analysis of the satellite-retrieved rainfall and moisture shows that in the spectral regions corresponding to WIGn1 and WIGn2 waves, precipitation is significantly correlated with lowlevel moisture but not with midlevel moisture. In contrast, significant coherence between rainfall and moisture at these levels is found in the spectral regions corresponding to the Kelvin and EIGn0 waves. These results may bear on different convection-wave coupling mechanisms for these ''divergent'' waves (stratiform instability versus moisture-stratiform instability).

Influence of the basic state zonal flow on convectively coupled equatorial waves

“…On the other hand, the amplitude of the power above the background associated with the estimated dispersion curves, that is, where in frequency and wave number the spectral peak is maximized, is more sensitive to these parameters, and therefore, it points to a potential limitation of our approach regarding the scale selection of CCEWs. Moreover, our analysis does not exclude the possibility that phase speeds are also modulated by moist processes such as shown in Roundy [], Dias and Pauluis [], Yasunaga and Mapes [], and Dias et al []. The robustness of the H eq is consistent with theories that state that the speed of propagation of CCEWs is determined by normal modes of the tropical atmosphere, for instance, to first‐order approximation, the second baroclinic mode from Matsuno's theory [ Mapes , ; Majda and Shefter , ; Raymond and Fuchs , ; Kuang , ].…”

A Moist Static Energy Budget Analysis of Quasi-2-Day Waves Using Satellite and Reanalysis Data

“…Haertel and Kiladis (2004), based on a two-dimensional inviscid primitive equation linearized about a basic state, showed that the wave dynamics and structure are well described by a superposition of the first and second modes. Yasunaga and Mapes (2012a) and Yasunaga and Mapes (2014) showed a significant coherence between rain and midtroposphere divergence in WIG waves, suggesting an importance of the second mode to the wave dynamics.…”

“…The first baroclinic theory explained the phase speed of CCEWs by a constant GMS (Emanuel et al 1994;Neelin and Yu 1994;Raymond et al 2009). Based on a cross-spectral analysis, Yasunaga and Mapes (2012a) and Yasunaga and Mapes (2014) showed that the fast and slow WIG waves have different moist processes. Takayabu (1994b) and Dias and Kiladis (2014) reported Doppler shifting by the background zonal wind in WIG waves.…”

Convectively coupled Kelvin waves in aquachannel simulations: 2. Life cycle and dynamical‐convective coupling

“…A “subdivision” approach has been recently presented by Yasunaga and Mapes [] for the analysis of faster versus slower components of observed CCEW. They started by identifying contiguous regions of faster and slower phase speeds on a normalized power spectrum, and the subsequent filtering and compositing were applied to the fast and slow components separately.…”